Selecting training data for neural networks

Abstract

Apparatuses, systems, and techniques to automatically select training data. In at least one embodiment, training data is automatically selected based on, for example, metadata associated with the training data.

What is claimed is: 
     
         1 . A processor, comprising:
 one or more circuits to cause one or more neural networks to be trained using training data automatically selected based, at least in part, on metadata associated with the training data.   
     
     
         2 . The processor of  claim 1 , wherein the one or more circuits are further to:
 obtain a plurality of training data;   process the plurality of training data into a set of groups based at least in part on the metadata; and   automatically select the training data from the plurality of training data based at least in part on the set of groups.   
     
     
         3 . The processor of  claim 2 , wherein a first group of the set of groups corresponds to a first combination of metadata values. 
     
     
         4 . The processor of  claim 2 , wherein the one or more circuits to select the training data are further to use one or more equation solvers to automatically select the training data. 
     
     
         5 . The processor of  claim 1 , wherein the one or more circuits are further to:
 obtain one or more labels corresponding to the training data; and   train the one or more neural networks using at least the one or more labels and the training data.   
     
     
         6 . The processor of  claim 1 , wherein the training data comprises one or more images captured from one or more vehicles. 
     
     
         7 . The processor of  claim 1 , wherein the metadata indicates one or more operational design domain (ODD) values. 
     
     
         8 . A system, comprising:
 one or more computers having one or more processors to cause one or more neural networks to be trained using training data automatically selected to cause the one or more neural networks to generate output data having one or more attributes.   
     
     
         9 . The system of  claim 8 , wherein the one or more processors are further to:
 obtain a set of training data and associated metadata;   parse the set of training data to calculate one or more subsets of training data based at least in part on the associated metadata; and   use one or more equation solvers to calculate one or more numbers of assets for the one or more subsets of training data.   
     
     
         10 . The system of  claim 9 , wherein the one or more processors are further to automatically select the training data from the one or more subsets of training data based at least in part on the one or more numbers of assets. 
     
     
         11 . The system of  claim 9 , wherein the one or more numbers of assets are based at least in part on one or more target proportions. 
     
     
         12 . The system of  claim 8 , wherein the training data comprises one or more images captured from one or more medical devices. 
     
     
         13 . The system of  claim 8 , wherein the output data comprises one or more classifications of one or more objects depicted in one or more images. 
     
     
         14 . The system of  claim 13 , wherein the one or more attributes comprise one or more accuracy values corresponding to the one or more classifications. 
     
     
         15 . A processor, comprising:
 one or more circuits to use one or more neural networks to generate output data based, at least in part, on training data automatically selected, based at least in part, on metadata associated with the training data.   
     
     
         16 . The processor of  claim 15 , wherein the one or more circuits are further to:
 obtain one or more images depicting one or more objects; and   use the one or more neural networks to generate the output data based on the one or more images.   
     
     
         17 . The processor of  claim 16 , wherein:
 the one or more neural networks include one or more object detection neural networks; and   the output data comprises data indicating one or more locations of the one or more objects.   
     
     
         18 . The processor of  claim 15 , wherein the metadata indicates one or more conditions of the training data. 
     
     
         19 . The processor of  claim 15 , wherein the one or more circuits are further to use one or more equation solvers to automatically select the training data, wherein the one or more equation solvers are based at least in part on a linear formulation. 
     
     
         20 . The processor of  claim 15 , wherein the training data comprises sensor data. 
     
     
         21 . A system, comprising:
 one or more computers having one or more processors to use one or more neural networks to generate output data based, at least in part, on training data automatically selected to cause the output data to have one or more attributes.   
     
     
         22 . The system of  claim 21 , wherein the one or more processors are further to use one or more solvers to automatically select the training data, wherein the one or more solvers are based at least in part on a quadratic formulation. 
     
     
         23 . The system of  claim 21 , wherein the one or more processors are further to use the one or more neural networks to generate the output data based at least in part on a set of images. 
     
     
         24 . The system of  claim 21 , wherein the output data comprises one or more results of the one or more neural networks. 
     
     
         25 . The system of  claim 24 , wherein the one or more attributes indicate one or more confidence values for the one or more results. 
     
     
         26 . The system of  claim 21 , wherein the training data comprises one or more frames of one or more videos. 
     
     
         27 . A machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to at least:
 cause one or more neural networks to be trained using training data automatically selected based, at least in part, on metadata associated with the training data.   
     
     
         28 . The machine-readable medium of  claim 27 , wherein the set of instructions further comprise instructions, which if performed by the one or more processors, cause the one or more processors to:
 obtain a distribution indicating one or more clauses; and   automatically select the training data based at least in part on one or more proportions indicated by the one or more clauses.   
     
     
         29 . The machine-readable medium of  claim 27 , wherein the set of instructions further comprise instructions, which if performed by the one or more processors, cause the one or more processors to:
 obtain one or more collections of assets corresponding to one or more operational design domain (ODD) values;   select a first asset from a first collection of assets;   compute one or more distances between the first asset and the first collection of assets; and   select a second asset based at least in part on the one or more distances, wherein the training data comprises the first asset and the second asset.   
     
     
         30 . The machine-readable medium of  claim 29 , wherein the one or more distances are based on a temporal distance or a spatial distance. 
     
     
         31 . The machine-readable medium of  claim 27 , wherein the training data is selected based at least in part on labelled training data used to train the one or more neural networks. 
     
     
         32 . The machine-readable medium of  claim 27 , wherein the set of instructions further comprise instructions, which if performed by the one or more processors, cause the one or more processors to:
 provide the training data to one or more labelling entities to obtain one or more labels;   cause the one or more neural networks to process the training data to calculate one or more results; and   update the one or more neural networks based at least in part on the one or more results and the one or more labels.   
     
     
         33 . The machine-readable medium of  claim 27 , wherein the training data comprises one or more images captured from one or more autonomous devices.